Mathematics + Computation + Science = Solutions (MCSS Symposium)

Friday, 5 September 2014 - 9:00am - 5:30pm

Where: IUPUI Campus Center, Room 405

The Institute for Mathematical Modeling and Computational Science (iMMCS) will be hosting a research symposium on Computational Biology, Computational Medicine, Computational Neuroscience and other Computational Scientific fields. Two nationally reputed external keynote speakers, IUPUI researchers, and an industry-academic panel are expected to attend. This symposium will highlight some of the research happening here at IUPUI and elsewhere throughout the field of Computational Science and Mathematical Modeling.

Dr. Grama's research interests span the areas of parallel and distributed computing algorithms and applications. His research on applications has focused on particle dynamics methods, their applications to dense linear system solvers, and fast algorithms for data compression and analysis. The mission of the Center for Science of Information is to advance science and technology through new quantitative understanding of the representation, communication and processing of information in biological, physical, social and engineered systems. In 2013, Grama was inducted as a Fellow into the American Association for the Advancement of Science.

Keynote Speaker: Dr. Per B. Sederberg, Faculty in the Department of Psychology at Ohio State University

Sederberg's primary research interests are the success and failures of human memory. These interests motivate his research group's work in the OSU Computational Memory Lab, which has the overarching goal of developing a comprehensive theory of memory formation and retrieval that links our rich cognitive behavior to its underlying neural mechanisms. His lab combines a number of approaches to uncover the neural correlates and develop computational models of these processes: they collect and perform multivariate analysis of neural data, including fMRI and EEG, they run large-scale behavioral experiments and they develop computational models to link neural activity and behavior and to guide their experiment.